【摘要】：【目的/意義】精準(zhǔn)預(yù)測與掌握輿情事件的發(fā)展,及時發(fā)現(xiàn)輿情中的潛在危機,對社會的長治久安具有重要意義�！痉椒�/過程】針對網(wǎng)絡(luò)輿情演化的不確定性、多變性與灰色性等特征,選取多個指標(biāo)數(shù)據(jù)建立多因素灰色模型(MGM(1,m))。同時,為提高預(yù)測結(jié)果的精確度,利用BP神經(jīng)網(wǎng)絡(luò)對多因素灰色模型的預(yù)測殘差進行修正,構(gòu)建基于殘差修正的多因素灰色模型,并結(jié)合"莆田系事件"對模型預(yù)測性能進行驗證�！窘Y(jié)果/結(jié)論】仿真結(jié)果表明,相對于單一序列GM(1,1)模型和無殘差修正的多因素灰色模型,殘差修正后的多因素灰色模型在網(wǎng)絡(luò)輿情預(yù)測上具有一定的優(yōu)勢。
[Abstract]:Objective: accurate prediction and mastery of the development of public opinion events and timely discovery of potential crises in public opinion are of great significance to the long-term stability of society. [methods / processes] in view of the uncertainty of the evolution of public opinion on the Internet, The multi-factor grey model (MGM (1, m). Was established by selecting multiple index data with the characteristics of variability and grayness. At the same time, in order to improve the accuracy of prediction results, BP neural network is used to modify the residual prediction of multi-factor grey model, and a multi-factor grey model based on residual correction is constructed. Combined with Putian event, the prediction performance of the model is verified. [results / conclusion] the simulation results show that compared with the single sequence GM (1,1) model and the multi-factor grey model without residual correction, the prediction performance of the model is verified. The multi-factor grey model with residual error correction has some advantages in the prediction of network public opinion.
【作者單位】： 福州大學(xué)經(jīng)濟與管理學(xué)院;
【分類號】：C912.63

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